Logistic Regression with Missing Values in the Covariates PDF Download
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Author: Werner Vach
Publisher: Springer Science & Business Media
ISBN: 1461226503
Category : Mathematics
Languages : en
Pages : 152
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Book Description
In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications. The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.
Author: Werner Vach
Publisher: Springer Science & Business Media
ISBN: 1461226503
Category : Mathematics
Languages : en
Pages : 152
Get Book Here
Book Description
In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications. The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.
Author: Marjorie Ireland
Publisher:
ISBN:
Category : Regression analysis
Languages : en
Pages : 242
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Book Description
Author: Marcia Dawn Watkins
Publisher:
ISBN:
Category : Regression analysis
Languages : en
Pages : 216
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Book Description
Author: Lan Huang
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages :
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Book Description
Missing covariate data often arise in various settings, including surveys, clinical trials, epidemiological studies, biological studies and environmental studies. Large scale studies often have large fractions of missing data, which can present serious problems to the data analyst. Motivated by real data applications, this dissertation addresses several aspects in modeling and analyzing data with missing covariates. First, we propose Bayesian methods for estimating parameters in generalized linear models (GLM's) with nonignorably missing covariate data. We specify a parametric distribution for the response variable given the covariates (GLM), a parametric distribution for the missing covariates, and a parametric multinomial selection model for the missing data mechanism. Then we characterize general conditions for the propriety of the joint posterior distribution of the parameters and extend two model selection criteria, weighted L measure and Deviance Information Criterion for model comparison in the presence of missing covariates. Second, we develop a novel modeling strategy for analyzing data with repeated binary responses over time as well as with time-dependent missing covariates. We use the generalized linear mixed logistic model for the repeated binary responses and then propose a joint model for time-dependent missing covariates using information from different sources. The Monte Carlo EM algorithm is developed for computing the maximum likelihood estimates. An extended version of the AIC criterion is proposed to identify factors of interest that may disrupt the cyclical pattern of flowering. Third, we develop an efficient Gibbs sampling algorithm to sample from the joint posterior distribution for the generalized linear mixed logistic model. Moreover, we propose a novel Monte Carlo method to compute a Bayesian model comparison criterion, DIC, for any variable subset model using a single Markov Chain Monte Carlo sample from the full model without sampling from the posterior distribution under each subset model. In the end, we provide a brief discussion of future research.
Author: Peter L. Bonate
Publisher: Springer Science & Business Media
ISBN: 1441994858
Category : Medical
Languages : en
Pages : 634
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Book Description
This is a second edition to the original published by Springer in 2006. The comprehensive volume takes a textbook approach systematically developing the field by starting from linear models and then moving up to generalized linear and non-linear mixed effects models. Since the first edition was published the field has grown considerably in terms of maturity and technicality. The second edition of the book therefore considerably expands with the addition of three new chapters relating to Bayesian models, Generalized linear and nonlinear mixed effects models, and Principles of simulation. In addition, many of the other chapters have been expanded and updated.
Author: Marc Lavielle
Publisher: CRC Press
ISBN: 1482226510
Category : Mathematics
Languages : en
Pages : 380
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Book Description
Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whol
Author: Stef van Buuren
Publisher: CRC Press
ISBN: 0429960352
Category : Mathematics
Languages : en
Pages : 444
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Book Description
Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.
Author: Hai-An Hsu
Publisher:
ISBN:
Category : Logistic regression analysis
Languages : en
Pages : 254
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Book Description
Logistic regression is one of the most important tools in the analysis of epidemiological and clinical data. Such data often contain missing values for one or more variables. Common practice is to eliminate all individuals for whom any information is missing. This deletion approach does not make efficient use of available information and often introduces bias.
Author: James Carpenter
Publisher: John Wiley & Sons
ISBN: 1119942276
Category : Medical
Languages : en
Pages : 368
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Book Description
A practical guide to analysing partially observeddata. Collecting, analysing and drawing inferences from data iscentral to research in the medical and social sciences.Unfortunately, it is rarely possible to collect all the intendeddata. The literature on inference from the resultingincomplete data is now huge, and continues to grow both asmethods are developed for large and complex data structures, and asincreasing computer power and suitable software enable researchersto apply these methods. This book focuses on a particular statistical method foranalysing and drawing inferences from incomplete data, calledMultiple Imputation (MI). MI is attractive because it is bothpractical and widely applicable. The authors aim is to clarify theissues raised by missing data, describing the rationale for MI, therelationship between the various imputation models and associatedalgorithms and its application to increasingly complex datastructures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partiallyobserved data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider whenanalysing incomplete data from both observational studies andrandomized trials. Provides a detailed discussion of the practical use of MI withreal-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactionswith multiple imputation, survival analysis, multilevel multipleimputation, sensitivity analysis via multiple imputation, usingnon-response weights with multiple imputation and doubly robustmultiple imputation. Multiple Imputation and its Application is aimed atquantitative researchers and students in the medical and socialsciences with the aim of clarifying the issues raised by theanalysis of incomplete data data, outlining the rationale for MIand describing how to consider and address the issues that arise inits application.
Author: Qian Li Xue
Publisher: LAP Lambert Academic Publishing
ISBN: 9783838321578
Category :
Languages : en
Pages : 148
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Book Description
Missing data often arises in regression analysis either by study design or stochastic censoring. Restriction of analysis to complete observations may yield biased inferences. Developing likelihood-based methods for analyzing missing data in a regression setting has largely focused on missing values in the dependent variable. In this book, we discuss two likelihood-based approaches to inference for the regression of multivariate categorical outcomes on a set of covariates when some of the covariate values are missing. Specifically, this research seeks to develop methodologies in the context of latent variable models that (i) synthesize multiple outcomes into an latent construct that is easily interpretable yet retains relevant heterogeneity in individual outcomes; (ii) account for measurement inaccuracy in observable outcomes; (iii) model the association between the latent construct and covariates; (iv) handle missing covariate data in both ignorable and nonignorable cases. This book should be of particular interest to psychosocial scientists and others who plan to use latent variables models, but are discouraged by the daunting analytical difficulties associated with missing data.
